Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos, Thanasis; Sarigiannidis, Panagiotis; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

doi:10.1016/j.cosrev.2020.100341

Cited by 178 publications

(85 citation statements)

References 161 publications

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“…In some circumstances, however, AI may not be able to replace grid operators. Although AI systems can be more exact, dependable and extensive, still, numerous obstacles exist to be overcome when using AI in a smart grid, as shown in Figure 12 [124,125].…”

Section: Big Data Analytics As Solutions To Achieve Sustainability In the Smart Gridmentioning

confidence: 99%

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

Kumar¹,

Kasinathan²,

Elavarasan

et al. 2021

Sustainability

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The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

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Section: Big Data Analytics As Solutions To Achieve Sustainability In the Smart Gridmentioning

confidence: 99%

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

Kumar¹,

Kasinathan²,

Elavarasan

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Machine learning focuses more on the methodology issues, while CV studies the application of technologies in real-world scenarios. Machine learning methods have been widely used in the CV field, such as the statistical machine learning represented by support vector machine (SVM) and the deep learning represented by artificial neural network (ANN) [24,25]. These two methods have played crucial roles in promoting the continuous development of CV technology in monitoring construction sites.…”

Section: Background 21 Overview Of Computer Visionmentioning

confidence: 99%

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

Liu

Meng

et al. 2021

Buildings

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The unsafe behavior of construction workers is one of the main causes of safety accidents at construction sites. To reduce the incidence of construction accidents and improve the safety performance of construction projects, there is a need to identify risky factors by monitoring the behavior of construction workers. Computer vision (CV) technology, which is a powerful and automated tool used for extracting images and video information from construction sites, has been recognized and adopted as an effective construction site monitoring technology for the identification of risky factors resulting from the unsafe behavior of construction workers. In this article, we introduce the research background of this field and conduct a systematic statistical analysis of the relevant literature in this field through the bibliometric analysis method. Thereafter, we adopt a content-based analysis method to depict the historical explorations in the field. On this basis, the limitations and challenges in this field are identified, and future research directions are proposed. It is found that CV technology can effectively monitor the unsafe behaviors of construction workers. The research findings can enhance people’s understanding of construction safety management.

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“…First, Suricata is used with the Quickdraw ICS signatures and specification rules developed during the project. Next, a set of ML/DL-based detectors [38] are responsible for discriminating cyberattacks and anomalies against a plethora of EPES protocols, such as Modbus/TCP, DNP3, IEC 61850 (Generic Object Oriented Substation Event (GOOSE)), IEC 60870-5-104, Message Queuing Telemetry Transport (MQTT) and Network Time Protocol (NTP). Moreover, there is a detector called Nightwatch, which is able to discriminate potential anomalies related to the entire SDN network based on the statistics given by the SDN-C. OPF constitutes another detector of XL-SIEM, ensuring the privacy of EPES/SG entities and transferring relevant security logs to XL-SIEM whether they are relevant violations.…”

Section: Xl-siem and Detectorsmentioning

confidence: 99%

SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture

Radoglou-Grammatikis

Sarigiannidis

Dalamagkas

et al. 2021

Digital

Self Cite

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The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

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Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Cited by 178 publications

References 161 publications

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture

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